Forward-backward analysis of RFID-enabled supply chain using fuzzy cognitive map and genetic algorithm

  • Authors:

  • Moon-Chan Kim;Chang Ouk Kim;Seong Rok Hong;Ick-Hyun Kwon

  • Affiliations:

  • Department of Information and Industrial Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Information and Industrial Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Information and Industrial Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

Supply chain is a non-deterministic system in which uncontrollable external states with probabilistic behaviors (e.g., machine failure rate) influence on internal states (e.g., inventory level) significantly through complex causal relationships. Thanks to Radio frequency identification (RFID) technology, real time monitoring of the states is now possible. The current research on processing RFID data is, however, limited to statistical information. The goal of this research is to mine bidirectional cause-effect knowledge from the state data. In detail, fuzzy cognitive map (FCM) model of supply chain is developed. By using genetic algorithm, the weight matrix of the FCM model is discovered with the past state data, and forward (what-if) analysis is performed. Also, when sudden change in a certain state is detected, its cause is sought from the past state data throughout backward analysis. Simulation based experiments are provided to show the performance of the proposed forward-backward analysis methodology.